{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import logging\n",
    "import os\n",
    "import random\n",
    "import sys\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.transforms.functional as TF\n",
    "from pathlib import Path\n",
    "from torch import optim\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from tqdm import tqdm\n",
    "from os import listdir\n",
    "from os.path import splitext, isfile, join\n",
    "import wandb\n",
    "from evaluate import evaluate\n",
    "from unet import UNet\n",
    "from utils.data_loading import BasicDataset, CarvanaDataset, ARCADEDataset\n",
    "from utils.dice_score import dice_loss\n",
    "import logging\n",
    "import numpy as np\n",
    "import torch\n",
    "from PIL import Image\n",
    "from functools import lru_cache\n",
    "from functools import partial\n",
    "from itertools import repeat\n",
    "from multiprocessing import Pool\n",
    "from os import listdir\n",
    "from os.path import splitext, isfile, join\n",
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from utils.dice_score import multiclass_dice_coeff, dice_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_path =  Path('../../dataset/arcade_challenge_datasets/dataset_phase_1/segmentation_dataset/seg_train')\n",
    "dir_img = root_path.joinpath('images')\n",
    "dir_mask = root_path.joinpath('masks')\n",
    "dir_checkpoint = Path('./checkpoints/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:03<00:00, 261.13it/s]\n"
     ]
    }
   ],
   "source": [
    "val_percent: float = 0.1\n",
    "dataset = ARCADEDataset(dir_img, dir_mask, 0.5, n_classes=26, n_channels=1)\n",
    "n_val = int(len(dataset) * val_percent)\n",
    "n_train = len(dataset) - n_val\n",
    "train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for f_ in range(1, 1000):\n",
    "    msk = np.array(Image.open(os.path.join(dir_img, f\"{f_}.png\")))\n",
    "    if len(msk.shape) != 3:\n",
    "        print(f_, msk.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 1, 256, 256]), torch.Size([5, 256, 256]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader_args = dict(batch_size=5, num_workers=os.cpu_count(), pin_memory=True)\n",
    "train_loader = DataLoader(train_set, shuffle=True, **loader_args)\n",
    "val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)\n",
    "\n",
    "for batch in train_loader:\n",
    "    images, true_masks = batch['image'], batch['mask']\n",
    "    break\n",
    "images.shape, true_masks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4e26a289a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[1, 0], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 256, 256]),\n",
       " tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8, 10, 11, 13, 14, 15, 16, 20, 21, 22,\n",
       "         25]))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(true_masks[1], cmap=\"gray\")\n",
    "true_masks.shape, torch.unique(true_masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNet(\n",
       "  (inc): DoubleConv(\n",
       "    (double_conv): Sequential(\n",
       "      (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (down1): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU(inplace=True)\n",
       "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down2): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU(inplace=True)\n",
       "          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down3): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU(inplace=True)\n",
       "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down4): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): ReLU(inplace=True)\n",
       "          (3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up1): Up(\n",
       "    (up): ConvTranspose2d(1024, 512, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up2): Up(\n",
       "    (up): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up3): Up(\n",
       "    (up): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up4): Up(\n",
       "    (up): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (outc): OutConv(\n",
       "    (conv): Conv2d(64, 26, kernel_size=(1, 1), stride=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class ARGS():\n",
    "    classes = 26\n",
    "    bilinear = False\n",
    "\n",
    "args = ARGS()\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "# Change here to adapt to your data\n",
    "# n_channels=3 for RGB images\n",
    "# n_classes is the number of probabilities you want to get per pixel\n",
    "model = UNet(n_channels=1, n_classes=args.classes, bilinear=args.bilinear)\n",
    "model = model.to(memory_format=torch.channels_last)\n",
    "model.to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 26, 256, 256]), torch.float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = model(images.to(device=device))\n",
    "out.shape, out.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 256, 256]),)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_masks.shape, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.3150, device='cuda:0', grad_fn=<NllLoss2DBackward0>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criterion(out, true_masks.to(device=device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f58f7af8700>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(F.one_hot(true_masks, model.n_classes)[0,:,:,0].cpu(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 26, 256, 256]), torch.Size([5, 26, 256, 256]))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "zeros = F.one_hot(torch.zeros_like(out).argmax(dim=1), model.n_classes).permute(0, 3, 1, 2).float().cuda()\n",
    "mask_true = F.one_hot(true_masks, model.n_classes).permute(0, 3, 1, 2).float().cuda()\n",
    "mask_pred = F.one_hot(out.argmax(dim=1), model.n_classes).permute(0, 3, 1, 2).float().cuda()\n",
    "\n",
    "mask_true.shape, mask_pred.shape\n",
    "# multiclass_dice_coeff(out, true_masks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import Tensor\n",
    "\n",
    "\n",
    "def _dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6):\n",
    "    # Average of Dice coefficient for all batches, or for a single mask\n",
    "    assert input.size() == target.size()\n",
    "    assert input.dim() == 3 or not reduce_batch_first\n",
    "\n",
    "    sum_dim = (-1, -2) if input.dim() == 2 or not reduce_batch_first else (-1, -2, -3)\n",
    "    print(sum_dim,input.dim(),input.shape)\n",
    "    inter = 2 * (input * target).sum(dim=sum_dim)\n",
    "    sets_sum = input.sum(dim=sum_dim) + target.sum(dim=sum_dim)\n",
    "    sets_sum = torch.where(sets_sum == 0, inter, sets_sum)\n",
    "\n",
    "    dice = (inter + epsilon) / (sets_sum + epsilon)\n",
    "    return dice.mean()\n",
    "\n",
    "\n",
    "def _multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6):\n",
    "    # Average of Dice coefficient for all classes\n",
    "    return _dice_coeff(input.flatten(0, 1), target.flatten(0, 1), reduce_batch_first, epsilon)\n",
    "\n",
    "\n",
    "def _dice_loss(input: Tensor, target: Tensor, multiclass: bool = False):\n",
    "    # Dice loss (objective to minimize) between 0 and 1\n",
    "    fn = _multiclass_dice_coeff if multiclass else _dice_coeff\n",
    "    return 1 - fn(input, target, reduce_batch_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-1, -2) 3 torch.Size([26, 256, 256])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.0055, device='cuda:0')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_multiclass_dice_coeff(mask_pred[0:1, :, :,:], mask_true[0:1, :, :,:], reduce_batch_first=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-1, -2, -3) 3 torch.Size([130, 256, 256])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.9709, device='cuda:0')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_multiclass_dice_coeff(zeros[:, :, :,:], mask_true[:, :, :,:], reduce_batch_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 84.78317049  84.15921525 106.69765864  80.9018364   91.38703195\n",
      "  70.46254448  97.82822033  99.77500566  93.22208925  98.66632595]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 中心值（均值）\n",
    "mean = 92.5\n",
    "\n",
    "# 方差范围\n",
    "variance_min = 87\n",
    "variance_max = 97\n",
    "\n",
    "# 计算标准差范围\n",
    "std_dev_min = np.sqrt(variance_min)\n",
    "std_dev_max = np.sqrt(variance_max)\n",
    "\n",
    "# 生成正态分布随机数\n",
    "# 假设生成1000个随机数\n",
    "N = 92\n",
    "random_numbers = np.random.normal(mean, std_dev_min, N)\n",
    "\n",
    "# 打印前10个随机数\n",
    "print(random_numbers[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_numbers_int = np.round(random_numbers).astype(int)\n",
    "random_numbers_int[random_numbers_int>98] = random.randint(83, 98)\n",
    "plt.hist(random_numbers_int, bins=30, density=True)\n",
    "csv_file_path = 'random_numbers.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('random_numbers.csv', 'w') as f:\n",
    "    for i in random_numbers_int:\n",
    "        f.write(str(i) + '\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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